OpenRouter alternatives: where to route next

OpenRouter got you started; these OpenRouter alternatives are where to route next. We compared 20 routers, gateways and inference clouds on cost, control, latency and compliance, with the same ten criteria for every entry and no paid placements. Answer ten questions in the route planner and get a shortlist built for your stack, not a generic top ten.

When to stay on OpenRouter: you are prototyping, you want one bill across hundreds of models, and a small fee is cheaper than running infrastructure. The planner says so too when that is the honest answer.

Every alternative, side by side

OpenRouter and the 19 alternatives on model access, modalities, hosting, routing and pricing shape.

Facts are checked against each vendor’s own documentation and pricing pages on 21 September 2026. Model counts move weekly, so treat them as orders of magnitude, not guarantees.

Coverage at a glance

Every alternative against twelve capabilities. Yes means the platform covers it as a first-class capability; No means it does not, with no half-credit for “on the roadmap”.

The 20 profiles

What each one actually is, who it suits, and the part their marketing page leaves out.

OpenRouter

A marketplace-style router in front of hundreds of hosted models, with a single OpenAI-compatible endpoint, credits-based billing and automatic fallbacks between upstream providers.

• Best for: trying many models quickly, side projects, teams that want the widest text-model catalogue without contracts.
• Watch out for: a per-request fee sits on top of provider prices, support is community-first, and speech, video and OCR coverage is thin compared with the text catalogue.
• Pricing shape: provider price + platform fee, prepaid credits.
• Site: openrouter.ai

LLM API

A managed router covering 396 models from 37+ providers behind one key, with per-model uptime monitoring, spend controls, and a white-label router other companies resell under their own brand.

• Best for: teams running mixed modalities in production, agencies and platforms that need one bill, and companies reselling AI access.
• Watch out for: no self-hosted edition; the gateway runs as a managed service.
• Pricing shape: Prepaid credits, pay per token, an invoice for every top-up.
• Site: the model catalogue

Together AI

An inference cloud running open-weight models on its own GPU fleet, plus fine-tuning and dedicated capacity for steady workloads.

• Best for: open-model workloads at volume where per-token price and throughput matter more than closed-model access.
• Watch out for: no access to closed frontier models from OpenAI or Anthropic through the same endpoint.
• Pricing shape: per token, plus hourly dedicated GPUs.
• Site: together.ai

Fireworks AI

A performance-focused inference platform for open-weight models, with speculative decoding, LoRA tuning and on-demand deployments.

• Best for: latency-sensitive production apps built on open models.
• Watch out for: the catalogue is narrower than a marketplace router and closed frontier models are not included.
• Pricing shape: per token, plus on-demand GPU hours.
• Site: fireworks.ai

Groq

Custom LPU hardware serving a curated set of open models at very high throughput through an OpenAI-compatible API.

• Best for: voice agents, streaming chat and anything where response speed is the product.
• Watch out for: a small catalogue, capacity limits at peak, and no cross-provider routing.
• Pricing shape: per token, free developer tier.
• Site: groq.com

DeepInfra

A low-cost inference provider with an OpenAI-compatible endpoint across open text, embedding, image and speech models.

• Best for: cost-sensitive batch and background workloads.
• Watch out for: fewer enterprise controls, lighter SLAs, no closed frontier models.
• Pricing shape: per token, pay as you go.
• Site: deepinfra.com

Novita AI

An inference cloud spanning open LLMs plus image, video and speech generation, also available as GPU instances.

• Best for: media-heavy products that also need text models at low cost.
• Watch out for: quality of service varies by model; enterprise paperwork is lighter than a hyperscaler’s.
• Pricing shape: per token or per generation.
• Site: novita.ai

Replicate

A hosting layer for thousands of community-published models, including image, video and audio pipelines, with custom model deployment.

• Best for: generative media, experimentation and running your own containerised model.
• Watch out for: cold starts, per-second billing that is hard to forecast, and inconsistent model maintenance.
• Pricing shape: per second of compute.
• Site: replicate.com

AWS Bedrock

Amazon’s managed model service with Anthropic, Meta, Mistral, Amazon and other families, IAM controls, VPC access and regional isolation.

• Best for: enterprises already on AWS that need procurement, data residency and compliance more than catalogue breadth.
• Watch out for: model availability differs by region, quotas need raising, and the developer experience is heavier than a router.
• Pricing shape: per token, on your AWS bill; provisioned throughput available.
• Site: aws.amazon.com/bedrock

Azure AI Foundry

Microsoft’s AI platform hosting OpenAI models alongside open and partner models, with private networking, content filters and EU data boundary options.

• Best for: Microsoft-committed enterprises needing OpenAI models with corporate compliance.
• Watch out for: deployment quotas per region, and the catalogue outside OpenAI is uneven.
• Pricing shape: per token or provisioned units, on the Azure bill.
• Site: azure.microsoft.com

Google Vertex AI

Google’s managed platform for Gemini plus a model garden of partner and open models, with grounding, tuning and regional endpoints.

• Best for: teams standardising on Gemini, or needing long-context and multimodal models under GCP contracts.
• Watch out for: authentication and project setup are heavier than an API key; pricing differs per modality.
• Pricing shape: per token or per media unit, on the GCP bill.
• Site: cloud.google.com/vertex-ai

Portkey

A gateway that sits between your app and any provider key you bring, adding routing rules, caching, retries, budgets, logs and guardrails. Available managed or self-hosted.

• Best for: platform teams that already hold provider contracts and need control, tracing and spend limits.
• Watch out for: you still buy the models elsewhere; it is a control plane, not a catalogue.
• Pricing shape: per request tiers, free developer plan, enterprise self-host.
• Site: portkey.ai

LiteLLM

An MIT-licensed Python SDK and proxy that normalises 100+ provider APIs into the OpenAI format, with virtual keys, budgets and logging. Run it in your own cluster.

• Best for: engineering teams that want full control, no vendor in the data path, and provider prices with no markup.
• Watch out for: you own uptime, upgrades and scaling; the enterprise features sit behind a paid tier.
• Pricing shape: free and open source, paid enterprise tier.
• Site: litellm.ai

Kong AI Gateway

Kong’s gateway extended with AI plugins for multi-provider routing, prompt guards, token rate limiting and semantic caching, deployable on-prem.

• Best for: enterprises that already standardise API traffic on Kong and want AI under the same policy layer.
• Watch out for: infrastructure-team territory, with no model catalogue and no consumer-grade onboarding.
• Pricing shape: open source core, enterprise licence.
• Site: konghq.com

Cloudflare AI Gateway

A thin edge layer that proxies calls to providers, adding analytics, caching, retries and rate limits, with Workers AI for models hosted by Cloudflare.

• Best for: teams on Cloudflare who want cheap observability and caching without changing providers.
• Watch out for: not a catalogue or a billing layer; governance features are lighter than dedicated gateways.
• Pricing shape: free tier, usage-based extras.
• Site: developers.cloudflare.com/ai-gateway

Anyscale

A managed Ray platform for serving and fine-tuning models on your own infrastructure, including deployment into your VPC.

• Best for: ML teams with custom models and existing cloud commitments.
• Watch out for: the heaviest setup on this list, with no ready-made catalogue or router.
• Pricing shape: platform fee on top of your own compute.
• Site: anyscale.com

Hugging Face Inference

Inference providers on the Hub route calls to partners such as Together, Fireworks and Novita behind one token, alongside dedicated endpoints.

• Best for: open-model research, prototypes and anything already anchored on the Hub.
• Watch out for: no closed frontier models, and production guarantees depend on the upstream partner.
• Pricing shape: free credits, then pass-through provider pricing.
• Site: huggingface.co

Requesty

A multi-provider routing layer with caching, fallbacks, per-user budgets and analytics aimed at cutting model spend.

• Best for: teams whose main pain is an unpredictable model bill.
• Watch out for: a younger platform with a smaller catalogue and shorter operating history.
• Pricing shape: usage fee on top of provider prices.
• Site: requesty.ai

Eden AI

An aggregator covering OCR, translation, speech, moderation and generative models from many vendors under a single key and invoice, with an EU base.

• Best for: products that need document, speech and translation AI alongside LLMs.
• Watch out for: task-oriented rather than model-oriented; per-call pricing can exceed direct provider rates.
• Pricing shape: per call, with a monthly plan.
• Site: edenai.co

Unify

A router that scores providers on live latency, throughput and cost, then sends each request to the endpoint that best matches your constraints.

• Best for: engineers who want routing decisions made on measurements rather than a fixed preference list.
• Watch out for: a smaller catalogue and community; best suited to text workloads.
• Pricing shape: provider price plus routing fee.
• Site: unify.ai

Short version, by situation

You are prototyping and want maximum choice. Stay on OpenRouter, or use Hugging Face Inference. The per-request fee is irrelevant at small volume and the catalogue is the widest there is.

You are in production across text, voice and video. LLM API covers all four modalities behind one key with uptime monitoring and one bill. Novita AI is the budget alternative if you can accept lighter guarantees.

Your security team wants nothing in the data path. LiteLLM self-hosted, or Kong AI Gateway if you already run Kong. You keep provider prices with no markup and own the uptime.

Procurement has to sign off. AWS Bedrock, Azure AI Foundry or Google Vertex AI: the model spend lands on a cloud contract you already have, with residency and compliance attached.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.llmapi.ai/v1",
    api_key="LLMAPI_KEY",
)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.llmapi.ai/v1',
  apiKey: 'LLMAPI_KEY',
});

curl https://api.llmapi.ai/v1/models \
  -H "Authorization: Bearer LLMAPI_KEY"

How we compared them

Ten criteria, applied the same way to every entry, from public documentation and hands-on use:

1. Model breadth and how fast new releases land
2. Modality coverage: text, vision, speech, video, OCR
3. Routing, retries and failover behaviour
4. Pricing shape: markup, credits, invoices, commitments
5. Spend controls, budgets and per-team reporting
6. Data residency and regional hosting
7. Self-hosting and the licence that comes with it
8. White-label and reseller support
9. Enterprise readiness: SLAs, SOC 2, procurement
10. Migration effort from an OpenAI-compatible endpoint

What we could not measure. Latency and uptime vary by region, model and hour, so we did not publish a single speed ranking: any number we quoted would be stale by the time you read it. Support quality is reported, not tested. Where a vendor does not publish a fact, the table says so instead of guessing.

Read the full methodology in how we compared 20 OpenRouter alternatives.